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<li><a class="reference internal" href="#">Faces recognition example using eigenfaces and SVMs</a></li>
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  <div class="sphx-glr-download-link-note admonition note">
<p class="admonition-title">Note</p>
<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-applications-plot-face-recognition-py"><span class="std std-ref">here</span></a> to download the full example code or to run this example in your browser via Binder</p>
</div>
<div class="sphx-glr-example-title section" id="faces-recognition-example-using-eigenfaces-and-svms">
<span id="sphx-glr-auto-examples-applications-plot-face-recognition-py"></span><h1>Faces recognition example using eigenfaces and SVMs<a class="headerlink" href="#faces-recognition-example-using-eigenfaces-and-svms" title="Permalink to this headline">¶</a></h1>
<p>The dataset used in this example is a preprocessed excerpt of the
“Labeled Faces in the Wild”, aka <a class="reference external" href="http://vis-www.cs.umass.edu/lfw/">LFW</a>:</p>
<blockquote>
<div><p><a class="reference external" href="http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz">http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz</a> (233MB)</p>
</div></blockquote>
<p>Expected results for the top 5 most represented people in the dataset:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 33%" />
<col style="width: 22%" />
<col style="width: 13%" />
<col style="width: 19%" />
<col style="width: 13%" />
</colgroup>
<thead>
<tr class="row-odd"><th class="head"></th>
<th class="head"></th>
<th class="head"></th>
<th class="head"></th>
<th class="head"></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>Ariel Sharon</p></td>
<td><p>0.67</p></td>
<td><p>0.92</p></td>
<td><p>0.77</p></td>
<td><p>13</p></td>
</tr>
<tr class="row-odd"><td><p>Colin Powell</p></td>
<td><p>0.75</p></td>
<td><p>0.78</p></td>
<td><p>0.76</p></td>
<td><p>60</p></td>
</tr>
<tr class="row-even"><td><p>Donald Rumsfeld</p></td>
<td><p>0.78</p></td>
<td><p>0.67</p></td>
<td><p>0.72</p></td>
<td><p>27</p></td>
</tr>
<tr class="row-odd"><td><p>George W Bush</p></td>
<td><p>0.86</p></td>
<td><p>0.86</p></td>
<td><p>0.86</p></td>
<td><p>146</p></td>
</tr>
<tr class="row-even"><td><p>Gerhard Schroeder</p></td>
<td><p>0.76</p></td>
<td><p>0.76</p></td>
<td><p>0.76</p></td>
<td><p>25</p></td>
</tr>
<tr class="row-odd"><td><p>Hugo Chavez</p></td>
<td><p>0.67</p></td>
<td><p>0.67</p></td>
<td><p>0.67</p></td>
<td><p>15</p></td>
</tr>
<tr class="row-even"><td><p>Tony Blair</p></td>
<td><p>0.81</p></td>
<td><p>0.69</p></td>
<td><p>0.75</p></td>
<td><p>36</p></td>
</tr>
<tr class="row-odd"><td><p>avg / total</p></td>
<td><p>0.80</p></td>
<td><p>0.80</p></td>
<td><p>0.80</p></td>
<td><p>322</p></td>
</tr>
</tbody>
</table>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">time</span> <span class="kn">import</span> <span class="n">time</span>
<span class="kn">import</span> <span class="nn">logging</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>

<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">GridSearchCV</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">fetch_lfw_people</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">classification_report</span>
<span class="kn">from</span> <span class="nn">sklearn.metrics</span> <span class="kn">import</span> <span class="n">confusion_matrix</span>
<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <span class="n">PCA</span>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">SVC</span>


<span class="nb">print</span><span class="p">(</span><span class="vm">__doc__</span><span class="p">)</span>

<span class="c1"># Display progress logs on stdout</span>
<span class="n">logging</span><span class="o">.</span><span class="n">basicConfig</span><span class="p">(</span><span class="n">level</span><span class="o">=</span><span class="n">logging</span><span class="o">.</span><span class="n">INFO</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="s1">&#39;</span><span class="si">%(asctime)s</span><span class="s1"> </span><span class="si">%(message)s</span><span class="s1">&#39;</span><span class="p">)</span>


<span class="c1"># #############################################################################</span>
<span class="c1"># Download the data, if not already on disk and load it as numpy arrays</span>

<span class="n">lfw_people</span> <span class="o">=</span> <span class="n">fetch_lfw_people</span><span class="p">(</span><span class="n">min_faces_per_person</span><span class="o">=</span><span class="mi">70</span><span class="p">,</span> <span class="n">resize</span><span class="o">=</span><span class="mf">0.4</span><span class="p">)</span>

<span class="c1"># introspect the images arrays to find the shapes (for plotting)</span>
<span class="n">n_samples</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="n">lfw_people</span><span class="o">.</span><span class="n">images</span><span class="o">.</span><span class="n">shape</span>

<span class="c1"># for machine learning we use the 2 data directly (as relative pixel</span>
<span class="c1"># positions info is ignored by this model)</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">lfw_people</span><span class="o">.</span><span class="n">data</span>
<span class="n">n_features</span> <span class="o">=</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>

<span class="c1"># the label to predict is the id of the person</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">lfw_people</span><span class="o">.</span><span class="n">target</span>
<span class="n">target_names</span> <span class="o">=</span> <span class="n">lfw_people</span><span class="o">.</span><span class="n">target_names</span>
<span class="n">n_classes</span> <span class="o">=</span> <span class="n">target_names</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Total dataset size:&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;n_samples: </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">n_samples</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;n_features: </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">n_features</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;n_classes: </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">n_classes</span><span class="p">)</span>


<span class="c1"># #############################################################################</span>
<span class="c1"># Split into a training set and a test set using a stratified k fold</span>

<span class="c1"># split into a training and testing set</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span>
    <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>


<span class="c1"># #############################################################################</span>
<span class="c1"># Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled</span>
<span class="c1"># dataset): unsupervised feature extraction / dimensionality reduction</span>
<span class="n">n_components</span> <span class="o">=</span> <span class="mi">150</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Extracting the top </span><span class="si">%d</span><span class="s2"> eigenfaces from </span><span class="si">%d</span><span class="s2"> faces&quot;</span>
      <span class="o">%</span> <span class="p">(</span><span class="n">n_components</span><span class="p">,</span> <span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
<span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="n">pca</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="n">n_components</span><span class="p">,</span> <span class="n">svd_solver</span><span class="o">=</span><span class="s1">&#39;randomized&#39;</span><span class="p">,</span>
          <span class="n">whiten</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;done in </span><span class="si">%0.3f</span><span class="s2">s&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">t0</span><span class="p">))</span>

<span class="n">eigenfaces</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">components_</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">n_components</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">))</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Projecting the input data on the eigenfaces orthonormal basis&quot;</span><span class="p">)</span>
<span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="n">X_train_pca</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="n">X_test_pca</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;done in </span><span class="si">%0.3f</span><span class="s2">s&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">t0</span><span class="p">))</span>


<span class="c1"># #############################################################################</span>
<span class="c1"># Train a SVM classification model</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Fitting the classifier to the training set&quot;</span><span class="p">)</span>
<span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="n">param_grid</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;C&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mf">1e3</span><span class="p">,</span> <span class="mf">5e3</span><span class="p">,</span> <span class="mf">1e4</span><span class="p">,</span> <span class="mf">5e4</span><span class="p">,</span> <span class="mf">1e5</span><span class="p">],</span>
              <span class="s1">&#39;gamma&#39;</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.0001</span><span class="p">,</span> <span class="mf">0.0005</span><span class="p">,</span> <span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.005</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">],</span> <span class="p">}</span>
<span class="n">clf</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span>
    <span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s1">&#39;rbf&#39;</span><span class="p">,</span> <span class="n">class_weight</span><span class="o">=</span><span class="s1">&#39;balanced&#39;</span><span class="p">),</span> <span class="n">param_grid</span>
<span class="p">)</span>
<span class="n">clf</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_pca</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;done in </span><span class="si">%0.3f</span><span class="s2">s&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">t0</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Best estimator found by grid search:&quot;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">best_estimator_</span><span class="p">)</span>


<span class="c1"># #############################################################################</span>
<span class="c1"># Quantitative evaluation of the model quality on the test set</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Predicting people&#39;s names on the test set&quot;</span><span class="p">)</span>
<span class="n">t0</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="n">y_pred</span> <span class="o">=</span> <span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_pca</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;done in </span><span class="si">%0.3f</span><span class="s2">s&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">t0</span><span class="p">))</span>

<span class="nb">print</span><span class="p">(</span><span class="n">classification_report</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">target_names</span><span class="o">=</span><span class="n">target_names</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">confusion_matrix</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_pred</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="n">n_classes</span><span class="p">)))</span>


<span class="c1"># #############################################################################</span>
<span class="c1"># Qualitative evaluation of the predictions using matplotlib</span>

<span class="k">def</span> <span class="nf">plot_gallery</span><span class="p">(</span><span class="n">images</span><span class="p">,</span> <span class="n">titles</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">n_row</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">n_col</span><span class="o">=</span><span class="mi">4</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;Helper function to plot a gallery of portraits&quot;&quot;&quot;</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mf">1.8</span> <span class="o">*</span> <span class="n">n_col</span><span class="p">,</span> <span class="mf">2.4</span> <span class="o">*</span> <span class="n">n_row</span><span class="p">))</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">bottom</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">left</span><span class="o">=.</span><span class="mi">01</span><span class="p">,</span> <span class="n">right</span><span class="o">=.</span><span class="mi">99</span><span class="p">,</span> <span class="n">top</span><span class="o">=.</span><span class="mi">90</span><span class="p">,</span> <span class="n">hspace</span><span class="o">=.</span><span class="mi">35</span><span class="p">)</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_row</span> <span class="o">*</span> <span class="n">n_col</span><span class="p">):</span>
        <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">n_row</span><span class="p">,</span> <span class="n">n_col</span><span class="p">,</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
        <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">images</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">)),</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">gray</span><span class="p">)</span>
        <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="n">titles</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">size</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
        <span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(())</span>
        <span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(())</span>


<span class="c1"># plot the result of the prediction on a portion of the test set</span>

<span class="k">def</span> <span class="nf">title</span><span class="p">(</span><span class="n">y_pred</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">target_names</span><span class="p">,</span> <span class="n">i</span><span class="p">):</span>
    <span class="n">pred_name</span> <span class="o">=</span> <span class="n">target_names</span><span class="p">[</span><span class="n">y_pred</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span><span class="o">.</span><span class="n">rsplit</span><span class="p">(</span><span class="s1">&#39; &#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
    <span class="n">true_name</span> <span class="o">=</span> <span class="n">target_names</span><span class="p">[</span><span class="n">y_test</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span><span class="o">.</span><span class="n">rsplit</span><span class="p">(</span><span class="s1">&#39; &#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
    <span class="k">return</span> <span class="s1">&#39;predicted: </span><span class="si">%s</span><span class="se">\n</span><span class="s1">true:      </span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">pred_name</span><span class="p">,</span> <span class="n">true_name</span><span class="p">)</span>

<span class="n">prediction_titles</span> <span class="o">=</span> <span class="p">[</span><span class="n">title</span><span class="p">(</span><span class="n">y_pred</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">target_names</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
                     <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">y_pred</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])]</span>

<span class="n">plot_gallery</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">prediction_titles</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">)</span>

<span class="c1"># plot the gallery of the most significative eigenfaces</span>

<span class="n">eigenface_titles</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;eigenface </span><span class="si">%d</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">eigenfaces</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])]</span>
<span class="n">plot_gallery</span><span class="p">(</span><span class="n">eigenfaces</span><span class="p">,</span> <span class="n">eigenface_titles</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">)</span>

<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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